Dynamics modeling.
In different communities, the task of dynamics modeling is named differently.
For 2D dynamics in images, optical flow methods [1]}, [2]}, [3]} have been widely studied and used. Scene flow methods [4]}, [5]}, [6]} aim to capture the dynamics of all points in 3D space densely, and are often time-consuming and inefficient.
For objects, non-rigid tracking or matching methods [7]}, [8]}, [9]}, [10]} study the dynamics of non-rigid objects. However, these methods often only track the observable surface rather then the complete shape space. Recently, neural implicit functions [4]}, [12]} have been applied to estimate 3D dynamics, which we adapt and further improve through conditioning on pixel-aligned temporally-aggregated visual features.
